A Two-Phase Approach for Model-Based Design of Experiments Applied in Chemical Engineering

Jan Schwientek (), Charlie Vanaret, Johannes Höller, Patrick Schwartz, Philipp Seufert, Norbert Asprion, Roger Böttcher and Michael Bortz
Additional contact information
Jan Schwientek: Fraunhofer ITWM
Charlie Vanaret: Fraunhofer ITWM
Johannes Höller: Fraunhofer ITWM
Patrick Schwartz: Fraunhofer ITWM
Philipp Seufert: Fraunhofer ITWM
Norbert Asprion: BASF SE
Roger Böttcher: BASF SE
Michael Bortz: Fraunhofer ITWM

A chapter in Operations Research Proceedings 2019, 2020, pp 513-519 from Springer

Abstract: Abstract Optimal (model-based) experimental design (OED) aims to determine the interactions between input and output quantities connected by an, often complicated, mathematical model as precisely as possible from a minimum number of experiments. While statistical design techniques can often be proven to be optimal for linear models, this is no longer the case for nonlinear models. In process engineering applications, where the models are characterized by physico-chemical laws, nonlinear models often lead to nonconvex experimental design problems, thus making the computation of optimal experimental designs arduous. On the other hand, the optimal selection of experiments from a finite set of experiments can be formulated as a convex optimization problem for the most important design criteria and, thus, solved to global optimality. Since the latter represents an approximation of common experimental design problems, we propose a two-phase strategy that first solves the convex selection problem, and then uses this optimal selection to initialize the original problem. Finally, we illustrate and evaluate this generic approach and compare it with two statistical approaches on an OED problem from chemical process engineering.

Keywords: Optimal design of experiments; Experiments selection; Nonlinear optimization (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-030-48439-2_62

Ordering information: This item can be ordered from
http://www.springer.com/9783030484392

DOI: 10.1007/978-3-030-48439-2_62

Access Statistics for this chapter

More chapters in Operations Research Proceedings from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().